Triple
T676293
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tyne and Wear Metro |
E13083
|
entity |
| Predicate | usesFormerRailwayAlignments |
P14629
|
FINISHED |
| Object | true |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: true | Statement: [Tyne and Wear Metro, usesFormerRailwayAlignments, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesFormerRailwayAlignments Context triple: [Tyne and Wear Metro, usesFormerRailwayAlignments, true]
-
A.
followsFormerRailroad
chosen
Indicates that something is aligned with or traces the route of a former railroad line.
-
B.
usesRailInfrastructureOf
Indicates that one entity operates on, accesses, or otherwise makes use of the rail infrastructure owned or managed by another entity.
-
C.
railwayLine
Indicates that there is a railway line connection or route associated with or passing through the referenced entity.
-
D.
railwayUse
Indicates that something is used as, or functions in the capacity of, a railway or rail-based transportation facility.
-
E.
railwayLineRole
Indicates the specific functional role or purpose that a railway line serves within a rail network or transportation system.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a4933d3bf88190972041cd8cf143b9 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a04b2ae881908a5c23453bef8572 |
completed | March 1, 2026, 8:23 p.m. |
| PD | Predicate disambiguation | batch_69a49d1bbd0c81909cfbec30bd17bde7 |
completed | March 1, 2026, 8:10 p.m. |
Created at: March 1, 2026, 7:36 p.m.